Optimization (Python/GAMS) for Economic Dispatch in Energy

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The Algorithmic Economist (PhD)

4:31:14

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1. Introduction

1. Introduction.mp4

06:11

2. Economic Dispatch with Energy Storage in a 1-bus grid

1. Introduction.mp4

02:02

2. Define the input data in Python.mp4

07:28

3. Define the model.mp4

06:02

4. Mathematical Formulation.mp4

11:08

5. Defining the decision variables.mp4

03:16

6. Defining the objective and constraints.mp4

11:10

7. Solving the model and making necessary plots.mp4

14:47

8. Modelling and solving in GAMS.mp4

11:22

9. Debugging in GAMS.mp4

08:13

3. Economic Dispatch with Storage and CO2

1. Solving the model in Python with Storage and CO2.mp4

10:57

2. Convexity of the objective function and the CO2 constraint.mp4

06:42

3. Modelling and solving in GAMS With Storage and CO2.mp4

01:43

4. Modelling in Pyomo Without Storage, with CO2.mp4

05:25

5. Modelling in GAMS Without Storage, with CO2.mp4

01:00

4. Economic Dispatch with Storage, wind and CO2

1. Modelling in Python With storage, wind and CO2.mp4

14:21

2. Modelling in Python No Storage, with wind & CO2.mp4

02:09

3. Modelling in GAMS.mp4

01:46

5. Conclusions & Code

1. GAMS Sending the results to Excel.mp4

04:29

2. Conclusions.mp4

03:48

6. Economic Dispatch with Storage in a 24-bus grid

1. Introduction and formulation.mp4

22:41

2. What is a topology of a power system.mp4

03:47

3. What is a reliability test system.mp4

01:37

4. What is the per-unit system.mp4

03:13

5. Modelling in Python.mp4

10:08

6. Modelling in Python Defining the constants.mp4

15:26

7. Defining more constants.mp4

12:18

8. Defining the decision variables.mp4

15:48

9. Defining the constraints.mp4

18:29

10. Optimal solution.mp4

12:20

11. Solving in GAMS.mp4

17:24

7. Solving without Storage

1. Python modelling.mp4

02:21

8. Conclusions

1. Overview.mp4

01:43

Description

Use Python, Pyomo and GAMS to develop optimization models for electricity grids with energy storage and renewables.

What You'll Learn?

IMPORTANT: At checkout, use this bonus code (remove spaces): 9 A A C 5 8 7 1 3 2 0 2 5 D B F 4 3 A 8
EBOOK: Find me on LinkedIn and message me 'hi from Udemy'. I will send you my ebook & my publications (PDF). My linkedin is: linkedin /in/spyrosgnl
Understand the fundamentals of economic dispatch and its importance in electricity grid management.
Learn to define input data, build models, and formulate mathematical representations for economic dispatch in Python/Pyomo.
Gain practical skills in solving economic dispatch models with storage and CO2 constraints using Python and GAMS.
Develop the ability to model and solve economic dispatch problems that include renewable energy sources such as wind.
Explore advanced concepts by modeling economic dispatch in a 24-bus grid, including understanding power system topology and reliability.
Obtain practical experience in debugging, optimizing, and exporting model results to Excel for further analysis.
If you need help, send me your questions at linkedin com/in/spyrosgnl

Who is this for?

Professionals working in the energy sector looking to enhance their skills in electricity grid management and renewable integration.

Quantitative developers who want to apply their technical skills to the field of energy system modeling and optimization, leveraging their expertise in data analysis and mathematical modeling.

Data scientists and analysts interested in applying their skills to energy system modeling and optimization.

Students studying electrical engineering, energy systems, or related fields who want to understand economic dispatch models.

Researchers focusing on energy economics, optimization, or renewable energy technologies.

Policy makers and regulatory professionals aiming to gain a deeper understanding of economic dispatch and its implications for renewable energy.

PhD candidates

Anyone with a general interest in energy management, renewable energy, and the technical aspects of electricity grid operation.

What You Need to Know?

Basic knowledge of Python programming is beneficial but not mandatory, as all necessary installations and tutorials will be covered in the course.

An interest in electricity grid management and renewable energy integration will enhance the learning experience.

No prior experience with Pyomo or GAMS is required, as the course includes comprehensive, step-by-step instructions for beginners.

More details

Description
Welcome to my best-seller course!
If you need help, you will find me on LinkedIn. Send me an invitation at:
linkedin com/in/spyrosgnl
or find me on my website
www.
algorithmiceconomist.
com

WHATÂ THISÂ COURSE ISÂ ABOUT
This course is designed to provide you with a deep understanding of economic dispatch, a crucial concept in the efficient operation of power systems. You'll start with a thorough introduction to the basics before diving into the specifics of economic dispatch with storage in a 1-bus grid. Â You will learn to define input data, build models, formulate mathematical representations, and solve these models using Python/Pyomo and GAMS, complete with practical debugging techniques.
As you progress, the course will guide you through more complex scenarios, including the integration of CO2 constraints and renewable energy sources such as wind. You'll gain experience modelling and solving economic dispatch problems in Python and GAMS, both with and without storage. This includes understanding the convexity of objective functions and how to effectively incorporate CO2 constraints. Then you will focus on economic dispatch in a more complex 24-bus grid, learning about power system topology, reliability test systems, and the per-unit system.
By the end of the course, you will have modelled, solved, and optimized various dispatch scenarios, and be able to export your results to Excel for further analysis.
Whether you're a student, researcher, or industry professional, this course offers valuable insights and skills to enhance your understanding and management of electricity grids with renewables and storage.
There are no prerequisites.Â The course uses Python, Pyomo, and GAMS, assuming no prior experience. The lectures are easy to follow because the videos are very detailed. Come back here every 6 - 12 months to watch the updated version, as the content is regularly updated.

Â BIOGRAPHY*
I have a PhD in Economics, from Imperial College London, and since 2012 I have been conducting research and consultancy in this field.
Feel free to connect with me on LinkedIn, which is /in/spyrosgnl
I founded the Algorithmic Economist, which offers education in data-driven economics, where my expertise lies. You will learn how to conduct economic analyses using optimization, machine learning, and data science.
My teaching style uses simple, easy-to-understand language and plenty of examples.
My vision is to democratize this knowledge and make it more accessible. There are currently very few tutorials in this domain, and academic publications/textbooks often use complex language and are quite outdated.
If you need support, please reach out to me on LinkedIn. I share additional resources from time to time.
Who this course is for:
Professionals working in the energy sector looking to enhance their skills in electricity grid management and renewable integration.
Quantitative developers who want to apply their technical skills to the field of energy system modeling and optimization, leveraging their expertise in data analysis and mathematical modeling.
Data scientists and analysts interested in applying their skills to energy system modeling and optimization.
Students studying electrical engineering, energy systems, or related fields who want to understand economic dispatch models.
Researchers focusing on energy economics, optimization, or renewable energy technologies.
Policy makers and regulatory professionals aiming to gain a deeper understanding of economic dispatch and its implications for renewable energy.
PhD candidates
Anyone with a general interest in energy management, renewable energy, and the technical aspects of electricity grid operation.

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I have a PhD in Economics, from Imperial College London, and since 2012 I have been conducting research and consultancy in this field.Feel free to connect with me on LinkedIn, which is /in/spyrosgnlI founded the Algorithmic Economist, which offers education in data-driven economics, where my expertise lies. You will learn how to conduct economic analyses using optimization, machine learning, and data science.My teaching style uses simple, easy-to-understand language and plenty of examples.My vision is to democratize this knowledge and make it more accessible. There are currently very few tutorials in this domain, and academic publications/textbooks often use complex language and are quite outdated.If you need support, please reach out to me on LinkedIn. I share additional resources from time to time.

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